In our model, we use a policy gradient algorithm [1]}
for training. In the training process, the policy gradient algorithm
uses the reward of environmental feedback to perform gradient descent
on the neural network parameters to learn. Consider an episode of
length \(T\) , the agent collects \(\left(s_{k},a_{k},r_{k}\right)\) 
at each step \(k.\)  The parameters \(\theta \)  of its policy \(\pi _{\theta }\left(s_{k},a_{k}\right)\) 
is updated as follows
\(\theta \leftarrow \theta +\alpha \sum _{k=1}^{T}\nabla _{\theta }\log \pi _{\theta }\left(s_{k},a_{k}\right)\left(\sum _{k^{\prime }=k}^{T}r_{k^{\prime }}-b_{k}\right)\) 
